1. The Field of the Invention
The present invention relates to systems and methods for controlling and monitoring equipment in control systems. Embodiments of the invention relate to the precise control of devices, particularly when physics of the system result in constitutive relations between input and output that are nonlinear or time varying. Furthermore, embodiments of the invention provide a means for tracking how input to output relations of the system drift with time, thus providing a means of monitoring the characteristic condition or health of equipment. Embodiments of the invention define, initialize, tune, and/or monitor a particularly efficient, log-spaced control map to identify how the relationship between the strength or duration of control effort is mapped to amounts device output change that result from the applied effort. The invention further embodies methods for accessing and using the map to generate control signals used to precisely control the output of a system or process. The invention also embodies methods for monitoring when the system under control may fail to operate properly as the result of time varying operating conditions or failing equipment health.
Adaptive methods embodied in the invention take into account local control effort sensitivity as part of a self-tuning update that can be enabled at the end of each step in the control process. This update method causes the control map to improve with use and provides for excellent convergence to the actual nonlinear relationship between control input and device output that is exhibited by a system. As the mapping converges to the actual relationship, system performance is optimized because the amount of effort needed to change the system output by a desired amount can be directly obtained from the map. As the mapping converges and tracks what may be a time-varying relationship due to equipment wear or changing operating conditions, drift from baseline conditions can be detected, monitored, and used to alert the operator when system maintenance will be required.
2. The Relevant Technology
There are many systems and environments where the output of a controlled device or process must be changed from one state to another. Many of these systems require precise control of the system output. Precision movement is needed, for example, in positioning the hardware that can read media such as heads in hard disk drives, controlling semiconductor fabrication equipment, controlling microscope tables, controlling machines that place surface mount parts onto printed wiring boards, positioning of process control valves, controlling arms and articulators in robotics, controlling biotechnology manufacturing equipment, and the like. The ability to precisely control the output of a system can produce better results in many applications. Precise position control for example can allow for increased information storage densities, quicker device access times, more rapid device assembly, more accurate machining, more accurate and rapid placement of parts, improved mixtures, more accurate flow control, improved manufacturing quality, improved yields, and the like.
In addition to providing a means for precise control, the ability to detect changes in system equipment by monitoring the relationships between system inputs and outputs can be useful, for example, in alerting an operator of the need for immediate or upcoming maintenance to avoid unplanned shutdowns.
Precise control is difficult to achieve for various reasons. One of the prominent challenges facing precise control in systems that control position is friction. While friction occurs in all mechanisms involving relative motion, friction is not constant and is not easy to accurately model. Friction, for example, can vary as a function of velocity, displacement, temperature, time, path, and the like. In fact, it is the non-linearity of friction that makes it difficult to improve the precision of position control systems. Friction makes precision control at low velocities or over short displacements particularly difficult.
In order to overcome or compensate for the effects of friction, conventional control often employs feedback control wherein the output of the system is measured and compared to the desired position. A difference between the desired position and present actual position signals the need for corrective action. A linear combination of the difference between the desired position and the present actual position, the derivative of that difference, and the integral of that difference are often used to calculate the strength of the corrective action. While this type of approach is straightforward, robust, and often effective for coarse control, the approach can be plagued by hunting, sticking, or slow or excessive windup action when attempting to achieve narrow tolerances.
Further improvements may be made by compensating for certain forces of the system, if those forces can be predicted. Feeding signals based on inputs directly into the system without basing those signals on output measurements is called feedforward control. Feedforward can augment feedback control by approximately matching or compensating for known forces. The challenge is that friction forces are difficult to predict accurately. Several different parameters are needed to define high-fidelity friction models. These parameters are used to represent sticking behaviors, sliding behaviors, displacement conditions, contact stiffness, energy dissipation, shearing actions, and the like. It can be appreciated by one of skill in the art that accurately modeling the multi-faceted effects of friction can be a complicated undertaking.
The development of models that account for the nonlinear effects of friction is a complex undertaking because of the many physical mechanisms that combine to create friction forces. Nonetheless, recent advances in friction modeling have improved the fidelity and range of prediction of friction. In fact, friction has been shown to be much more repeatable than previously thought. However identifying the several parameters needed for the new models is not a trivial exercise.
Even if the friction behaviors are well understood and modeled, a system designer is still faced with the task of designing a controller that can estimate all the system states needed in order to use a high-fidelity friction model that accurately predicts the highly variable, multifaceted behavior of friction. The behavior and nonlinearity of friction can be especially pronounced when small movements or low velocities are required in a given system—the region of operation where most precise control must occur.
Some of the common control problems that are related to friction include steady-state errors, limit cycles, and stick-slip behavior. Steady state errors often occur, for example, when the control effort is in some way proportional to the control error. In this case as the process is driven closer to target, the control error is reduced. However, the control effort, which is proportional to the control error, is also reduced. At some point of the process, the control effort ceases to exceed the force of friction and the system decelerates to rest, often shy of the target or outside of tolerance.
Limit cycles can result from attempts to overcome the steady state error when integral control is added to the proportional control approach described above. The force from integral control builds up enough to overcome the force of friction. However, a system with integral control does not change its integrated value quickly. As a result, motion in the appropriate direction may be achieved, but the target is often overshot. The system then repeats the integral control process in the opposite direction, which results in the target being overshot in the opposite direction. This process can occur indefinitely.
Stick-slip behavior describes the condition that often occurs when making fine position adjustments. The force that is applied to a load in order to overcome the static friction also causes an acceleration of the load when the slip occurs. In other words, the force of friction drops suddenly to a kinetic value when the slip occurs and the force needed to overcome the initial static friction is not immediately extinguished causing an acceleration of the load. Stick-slip behavior can be problematic when small displacements or low velocities are required because friction force is especially dynamic when transitioning between stuck and sliding states that are typically associated with small movements.
The nonlinear effects of friction pose a great challenge to fine position control and improvements are needed in control systems and methods of performing control effort calculations to meet the demands for precise control with greater efficiency and speed.
Furthermore, a long standing challenge in the control field has been to develop methods that can accurately warn a system operator when maintenance will be needed so that maintenance can be scheduled before a failure occurs, thus minimizing costly unscheduled downtimes.